1. Field of Invention
The present invention relates to electronics and more specifically, to techniques for improving the linear performance of electrical components such as analog-to-digital converters and power amplifiers in radio frequency (RF) transceiver systems.
2. Description of Related Art
An electronic or power amplifier is a device for increasing the power of an input signal. Power amplifiers in most electronic systems are required to be “linear,” in that they must accurately reproduce the signal present at their input to achieve efficiency and spectral purity. An amplifier that compresses its input or has a non-linear input/output relationship causes the output signal to splatter onto adjacent frequencies. This causes interference on other frequency channels. Power amplifiers tend to become more non-linear as their power increases towards their maximum rated output.
Power amplifier linearity and efficiency are crucial issues in the design of electronic systems. The power amplifier is one of the most import parts of and usually the largest single contributor to the power consumption of an RF system. Thus, it is desirable to maximize the efficiency of a power amplifier. However, the more efficient power amplifier configuration is used the more nonlinear it usually becomes. Linearity errors in power amplifiers cause harmonic distortion and intermodulation distortion, which can limit the performance of state-of-the-art electronic systems such as, but not limited to radar systems, digital transceivers for wireless 3G and 4 G communications, laboratory test equipment, medical imaging, and audio and video compression. Reducing errors in digital-to-analog converters, analog-to-digital converters, sample-and-hold circuitry, and buffer and power amplifiers can significantly improve the performance of the critical conversion process.
An analog-to-digital converter (ADC) is a device for converting continuous radio frequency signals into discrete-time sampled, quantized data for subsequent digital processing. Like power amplifiers, analog-to-digital converters in most electronic systems are required to be “linear,” in that they must accurately reproduce the signal present at their input to provide a high-resolution digitized version at its output. An analog-to-digital converter with a non-linear transfer function will introduce distortion components, such as harmonic or intermodulation distortion, that limit the effective resolution and dynamic range of the analog-to-digital conversion.
Several common linearization methods exist for improving the linearity of devices such as power amplifiers and analog-to-digital converters, all of which suffer from drawbacks. All conventional linearization methods are limited in their maximum correctable range, which is the region of power output level near the onset of saturation. One method, known as a feedforward technique, is frequently employed and improves linearity, but results in poor power amplifier efficiency.
Predistortion is another technique used to improve the linearity of amplifiers. Digital predistortion is used to linearize the nonlinear response of a power amplifier over its intended power range. A predistortion circuit inversely models an amplifier's gain and phase characteristics and, when combined with the amplifier, produces an overall system that is more linear and reduces the amplifier's distortion. In essence, “inverse distortion” is introduced into the input of the amplifier, thereby cancelling to some degree any non-linearity the amplifier might have. However, the effectiveness of any predistortion technique is directly dependent on the accuracy of the predistortion transfer function, i.e., the model of the amplifier's gain and phase distortion characteristics. Traditional predistortion techniques implement a second-order or third-order polynomial as the transfer function, which improves the linearity of a power amplifier. However, for advanced RF systems with very high instantaneous bandwidths, transfer functions based on second-order or third-order polynomials are not accurate enough to prevent all non-linear distortion. In fact, many RF devices produce irregular nonlinearities that are difficult to model with standard, integer-power polynomial functions. Moreover, traditional predistortion techniques typically employ only one transfer function for a power amplifier, which may be suitable for a particular set of operating conditions. However, when operating conditions vary, e.g., temperature, time, or frequency, the transfer function may no longer be suitable to adequately remove non-linear distortion. Accordingly, there is a need for an improved linearization technique that implements higher-order transfer functions and accounts for varying operating conditions.
Digital post-processing is another technique used to improve the linearity of analog-to-digital converters. Digital post-processing is used to linearize the nonlinear response. Digital post-processing inversely models an analog-to-digital converter's nonlinear distortion transfer function such that the deleterious nonlinear distortion components can be subtracted from the output of the analog-to-digital converter to produce an overall system that is more linear and reduces the nonlinear distortion. In essence, “inverse distortion” is introduced into the output of the analog-to-digital converter, thereby cancelling to some degree any non-linearity the analog-to-digital converter might have. However, the effectiveness of any digital post-processing technique is directly dependent on the accuracy of the nonlinear distortion transfer function, i.e., the model of the analog-to-digital converter's nonlinear distortion characteristics. Traditional digital post-processing techniques implement a second-order or third-order polynomial as the transfer function, which improves the linearity of an analog-to-digital converter. However, for advanced RF systems with very high instantaneous bandwidths, transfer functions based on second-order or third-order polynomials are not accurate enough to prevent all non-linear distortion. Traditional higher-order or more complex models, such as Volterra-based compensation techniques, require a prohibitive amount of signal processing resources (such as multiply-accumulate functions which increases the physical size, weight, power, and cost of the hardware implementation of the digital post-processing). Accordingly, there is a need for an improved linearization technique that implements more complex transfer functions to provide the necessary linearization performance with a reasonable amount of signal processing resources.